Modeling, Simulation and Training -- Projects

Airport Demand/Capacity Model

Automated Discovery of Innovative Tactics and Behaviors

Biotechnology and Computational Biology

Creating Virtual Distributed C2 Nodes

Distance Learning with Intelligent Agents

Flexible Simulation Capability for Terminal Airspace

Nanotechnology Trends in Materials and Their Impact on Aviation

Next Generation Model of the National Airspace System

Modeling, Simulation and Training

Modeling, Simulation and Training focuses on information technology to support training, and the technology and innovative application of modeling and simulation. The information revolution is fueling changes in the workplace at an unprecedented rate, and these changes are threatening to overwhelm conventional education and training approaches. Fortunately, advanced instructional technologies like embedded training and collaborative learning environments can help warfighters and intelligence analysts adapt to these changes. Advances in simulation infrastructure, interoperability architectures, and modeling paradigms have simplified the application of simulation, demonstrated the feasibility of building simulations from reusable components, and otherwise facilitated a revolution in simulation application.

Airport Demand/Capacity Model

Ashley Williams, Principal Investigator

Washington

Problem
The FAA wants to explore various policy-based solutions to capacity and demand imbalances in the nation’s commercial airports. To evaluate such policy initiatives, the FAA needs to answer many specific questions regarding the likely outcome of such changes. To answer these questions, the likely strategies airport users will employ in the anticipated environment must be modeled to high fidelity.

Objectives
We will construct a model of the envisioned airport environment that can anticipate changes to the schedules of current and future airport users. This will facilitate policy-relevant predictions of such factors as changes to the average fares passengers will face, the number of destinations served, and the number of carriers at the airport.

Activities
The model, begun in FY02 under the “Aviation Demand and Performance Analysis” project, will be completed and validated. The initial application will be for LaGuardia Airport because of the relatively simple airline usage there. The airport model will then be applied to Chicago O’Hare as a final proof of concept because the likelihood that schedules will be much more complex.

Impacts
This research will improve MITRE's ability to understand and quantify the potential impacts of various policy-based solutions to capacity and demand imbalances on the nation's commercial airports.

Automated Discovery of Innovative Tactics and Behaviors

Lashon B. Booker, Principal Investigator

Washington

Problem
Modeling and simulation plays a key role in the design, analysis, and implementation of new military concepts and systems. Effective modeling in this context requires the capability to quickly generate innovative twists on operational concepts, tactics, and possible threat responses. Currently, the only possibilities examined are those few that happen to come to mind for the human designers and analysts.

Objectives
Any technique that enables humans to systematically examine a broader range of options, or suggests alternatives they may not have considered, would greatly increase the effectiveness of these simulation-based activities. We will develop new machine learning techniques to address this need. Our hypothesis is that innovative tactics and behaviors can be learned automatically from experience in a simulation.

Activities
The research has already developed techniques that can learn rule-based reactive behaviors given feedback about outcomes. That capability is being extended to learn more structured, distributed behaviors (e.g., those requiring teamwork). The final improvement will address the knowledge representations needed to learn coordinated tactics in challenging simulated environments (e.g., RoboCup soccer and micro-air vehicle swarms).

Impacts
This research will develop new capabilities that will enhance the effectiveness of simulation technology in critical applications such as simulation-based acquisition and joint experimentation. If successful, these developments will also advance the state of the art in machine learning and produce several refereed publications.

Presentation    PDF

Biotechnology and Computational Biology

Jordan C. Feidler, Principal Investigator

Washington

Problem
Biological agents present a significant challenge to homeland security and defense of the warfighter in asymmetric environments. The difficulty in dealing with biological threats is compounded by the relatively low barriers to entry to produce novel pathogenic agents. Improved techniques and methods combined with basic-level training can be exploited to aid in the design of new pathogens with increased virulence.

Objectives
This work will speed response to a novel pathogenic agent using computational modeling techniques to quickly identify how a biological agent acts to disrupt normal cellular processes. Our technical approach entails a process of iterative refinement whereby modeling and experimentation drive each other to increase our understanding of a particular cellular pathway commonly perturbed by biological warfare agents: FAS-mediated cell suicide.

Activities
Our initial focus is on creating a computational model of the FAS-mediated cell death pathway, which is disturbed by a number of biological warfare agents. We are working in collaboration with the Molecular Pathology Department at the Walter Reed Army Institute of Research, where we receive training on the experimental techniques that will be required to test the models.

Impacts
Computational models will allow for rapid estimation of how pathogenic a novel agent may be, so that countermeasures can be mounted that are commensurate with the posed threat. They will also shorten the time required to develop a possible pharmacological or antibiotic treatment by allowing researchers to explore alternative hypotheses in simulation and prioritize experimental approaches.

Creating Virtual Distributed C2 Nodes

Edward C. Wigfield, Principal Investigator

Bedford and Washington

Distance Learning with Intelligent Agents

Brad Goodman, Principal Investigator

Bedford and Washington

Problem
Classroom learning improves significantly when students participate in learning activities in small groups of peers. As the U.S. military moves from schoolhouse instruction to Web-based distance learning, students risk losing this important opportunity to collaborate with other students. Adding conventional groupware tools, such as chat and email, is a start, but these tools do not necessarily remove the deficiencies.

Objectives
This project will develop and insert a learning agent into a collaborative distance-learning environment to promote interaction amongst students and help warriors become better thinkers. Collaboration tools allow multiple students to participate together from a distance, but they cannot guarantee quality interaction. We will develop a learning agent capable of acting as a peer with the students to enhance learning.

Activities
A learning agent will be developed that plays different instructional roles. The agent will observe and manipulate the environment, as well as communicate directly with students. Research in multi-agent planning and studies on paradigms for instructional support in collaborative learning groups will be conducted to determine the proper roles of learning agents. Finally, empirical evaluations of the learning agent will be performed.

Impacts
The proposed research will provide a new and more effective foundation for the Web-based distance learning programs underway in the military. Our intelligent system and collaborative learning research has already spawned a new Army program in companion-based learning and has been applied to a number of research prototypes.

Flexible Simulation Capability for Terminal Airspace

Justin Boesel, Principal Investigator

Washington

Problem
Aviation simulation tools lack the flexibility to adequately model proposed changes to Terminal Radar Approach Control (TRACON) operations. Existing tools have highly structured conceptual models that can work well only if the structure matches the situation one would like to model. As a result, benefit analyses of innovative procedures are often difficult to carry out.

Objectives
This project will develop new simulation capabilities (mainly algorithms and modeling approaches implemented in the SLX language) that experienced modelers can use, alter, and combine in creative ways to answer tough questions in a relatively short time. One-day classes to teach the use of these new tools will also be held.

Activities
Using the SLX simulation language and visualization tools such as MapInfo and Proof Animation, we will develop the ability to model situations such as procedures involving departure fixes shared by multiple airports in larger TRACONs; integration of TRACON traffic into the overflight stream; and the impact of improved navigation accuracy, reduced separations, and shorter final approach procedures.

Impacts
These modeling tools will enhance MITRE's ability to gauge the impacts of proposed changes to TRACON operations, and enhance our bench strength in rapid model development using SLX.

Nanotechnology Trends in Materials and Their Impact on Aviation

Sarah E. O’Donnell, Principal Investigator

Washington

Problem
As nanotechnology influences materials engineering, a new breed of aircraft materials influences the possibilities for robust, second-generation commercial aircraft with new flight envelopes and versatile flight profiles. What nanotechnologies enable adaptive wing vehicles with massively redundant systems? How will the NAS evolve when vehicles can adapt to a dynamic environment? How does this influence the future vision of aviation?

Objectives
This investigation identifies new aircraft performance characteristics resulting from nanotechnology advances and the potential propagation of these effects through the NAS. The research continues to study the use of carbon nanotube reinforced polymers in aircraft structures and the potential to reduce wake vortex formation. Additional work includes following academic, industry, and government trends in smart materials, molecular electronics, nanosensors, and other enabling innovations in nanotechnology.

Activities
The work focuses on addressing issues surfacing from a comprehensive review of previous efforts in aviation nanotechnology. An expansion of earlier calculations includes a more complete set of wake vortex separation matrices. This analysis, and the potential impact of new separations at current airports, will be captured in a MITRE report.

Impacts
Ultra-strong, super-light materials potentially enhance the safety and security of an airframe, leading to safe reductions in vortex separation standards. Massively redundant systems may enable real-time health monitoring of the entire aircraft, similar to a nervous system. Nanotechnology may produce enhancements in adaptive materials, leading to airframes with innate information processing capabilities and active flow control that optimizes flight performance.

Next Generation Model of the National Airspace System

Frederick Wieland, Principal Investigator

Washington

Problem
The FAA has been asking increasingly difficult questions of the form “If a change X occurs, what is the system-wide impact as measured by metric M?” These questions generally are too detailed for statistically abstract models, and emulative models are too narrow in scope to provide system-wide answers. We need a model midway between statistical and emulative models to run future analyses effectively.

Objectives
We will produce an “actor-based” model of the National Airspace System (NAS), where the actors represent the major system components. The model will be engineered to be both scalable and extensible. For robust studies, system-wide models typically require rapid processing of 60,000–100,000 flights. We will also project future traffic patterns to help assess the impact of planned NAS changes.

Activities
The general strategy is to quickly reproduce traditional, statistically based system-wide models of the NAS, and to verify the actor-based implementation against them. Extensions that include advanced algorithms for ATC and provisions for future vision work will be added to the model in successive stages. The result will be a working model in the first quarter of the project, to be incrementally developed thereafter.

Impacts
This model will help to answer customer questions that require system-wide analysis. Issues such as time-phased implementation of planned infrastructure improvements, tradeoffs of alternative air traffic management strategies, assessment of future vision proposals, and the impact of imperfect information on planning functions are all expected to be addressable with this model.